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Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification

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arxiv 2010.10644 v4 pith:HY2JN4BE submitted 2020-10-20 cs.LG cs.AIstat.ML

Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification

Daniel J. Mankowitz , Dan A. Calian , Rae Jeong , Cosmin Paduraru , Nicolas Heess , Sumanth Dathathri , Martin Riedmiller , Timothy Mann This is my paper
classification cs.LG cs.AIstat.ML
keywords misspecificationconstrainedconstraintscontroldomaineffectslearningmodel
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Many real-world physical control systems are required to satisfy constraints upon deployment. Furthermore, real-world systems are often subject to effects such as non-stationarity, wear-and-tear, uncalibrated sensors and so on. Such effects effectively perturb the system dynamics and can cause a policy trained successfully in one domain to perform poorly when deployed to a perturbed version of the same domain. This can affect a policy's ability to maximize future rewards as well as the extent to which it satisfies constraints. We refer to this as constrained model misspecification. We present an algorithm that mitigates this form of misspecification, and showcase its performance in multiple simulated Mujoco tasks from the Real World Reinforcement Learning (RWRL) suite.

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